Quantitative Decomposition of Prediction Errors Revealing Multi-Cause Impacts: An Insightful Framework for MLOps

Keita Sakuma, Ryuta Matsuno, Yoshio Kameda. Quantitative Decomposition of Prediction Errors Revealing Multi-Cause Impacts: An Insightful Framework for MLOps. In Ingo Frommholz, Frank Hopfgartner, Mark Lee 0001, Michael Oakes 0001, Mounia Lalmas, Min Zhang 0006, Rodrygo L. T. Santos, editors, Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023. pages 4259-4263, ACM, 2023. [doi]

@inproceedings{SakumaMK23-0,
  title = {Quantitative Decomposition of Prediction Errors Revealing Multi-Cause Impacts: An Insightful Framework for MLOps},
  author = {Keita Sakuma and Ryuta Matsuno and Yoshio Kameda},
  year = {2023},
  doi = {10.1145/3583780.3615238},
  url = {https://doi.org/10.1145/3583780.3615238},
  researchr = {https://researchr.org/publication/SakumaMK23-0},
  cites = {0},
  citedby = {0},
  pages = {4259-4263},
  booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023},
  editor = {Ingo Frommholz and Frank Hopfgartner and Mark Lee 0001 and Michael Oakes 0001 and Mounia Lalmas and Min Zhang 0006 and Rodrygo L. T. Santos},
  publisher = {ACM},
}